Cautionary Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

  • Abstract
  • Keywords
  • References
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  • Abstract

    In this paper, 19 cautionary traffic signs were selected as a database and 3 types of conditions have been proposed. The conditions are 5 different time of image taken; hidden region and anticlockwise rotation are all the experiments design that will shows all the errors in producing the it’s mean value and the performance of traffic sign recognition. Initial hypothesis was made as the error will become larger as the interruption getting bigger. Based on the results of the five-different time of image taken, the error gives the best performance; less error when time is between 8am to 12am due to the brightness factors and the sign can be recognize clearly during noon session. The hidden region conditions show good performances of the detection and recognition of the system depend on the lesser coverage of the hidden region introduce on traffic sign because if the hidden region coverage is huge the database will get confuse and take a longer time to do the recognition process. Lastly, in anticlockwise rotation shows that 90o gave large value of error causing the system unable to recognize sign perfectly rather than 135o angle. To sum-up, detection and recognition process are not depending on higher number of angle but the process solely depending on their value of sample for each traffic signs. The error will give the impact towards traffic sign recognition and detection process. In conclusion, SNN can perform the detection and recognition process to all objects as in the future the system will become more stable with the right technique on spiking models and well-developed technology in this field.



  • Keywords

    SNN; traffic sign; hidden region; rotational; five-different-time image taken; mean error; detection; recognition.

  • References

      [1] W. Maass, T. Natschl¨ager, and H. Markram. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14(11), 2531–2560, 2002.

      [2] Sophie Deneve. Bayesian inference in spiking neurons. In Lawrence K. Saul, Yair Weiss and L´eon Bottou (Eds.), Advances in Neural Information Processing Systems 17. Cambridge: MIT Press, 2005, pp. 353–360.

      [3] Rajesh P. N. Rao. Hierarchical Bayesian inference in networks of spiking neurons. In Lawrence K. Saul, Yair Weiss and L´eon Bottou (Eds.), Advances in Neural Information Processing Systems 17. Cambridge: MIT Press, 2005, pp. 1113–1120.

      [4] Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, and Peter Dayan. Probabilistic computation in spiking populations. In Lawrence K. Saul, Yair Weiss and L´eon Bottou (Eds.), Advances in Neural Information Processing Systems 17. Cambridge: MIT Press, 2005, pp. 1609–1616.

      [5] Sander M. Bohte A Joost N. Kok B. Applications of Spiking Neural Networks. Journal Information Processing Letters, Vol. 95 Issue 6, 519-520, 2005.


      [7] Laman Interaktif PDRM,

      [8] Manual Traffic Signs: Standard Sign colors,

      [9] Maass, W. Networks of Spiking Neurons: The Third Generation of Neural Network Models. Neural Networks, 10(9), 1659–1671, 1997.

      [10] Maass, W. Computing with spiking neurons. In: Maass, W., Bishop, C.M. (Eds.), Pulsed Neural Networks. Cambridge: MIT Press, 1999.

      [11] Gerstner, W., Kistler, W.M. Spiking neuron models. Cambridge University Press, 2002.

      [12] Knesek, E.A.: Roche image analysis system. Acta Cytologica, 40(1), 60–66, 1996.

      [13] Lezoray, O., Cardot, H. Cooperation of pixel classification schemes and color watershed: A Study for Microscopical Images. IEEE Transactions on Images Processing, 11(7), 738–789 (2002).

      [14] Wu, H.S., Barba, J., Gil, J. Iterative thresholding for segmentation of cells from noisy images. J. Microsc. 197, 296–304 (2000).

      [15] Mouroutis, T., Roberts, S.J., Bharath, A.A. Robust cell nuclei segmentation using statistical modeling. BioImaging, 6, 79–91 (1998).

      [16] Papanicolaou, G.N. A new procedure for staining vaginal smears. Science, 95, 432 (1942).

      [17] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital image processing using MATLAB. 2009.

      [18] Karla Brkic. An overview of traffic sign detection methods, 2011.

      [19] Hunter, Richard Sewall. (1948). Photoelectric Color-Difference Meter. JOSA. 38(7): 661. In Winter Meeting of the Optical Society of America.

      [20] 3D representations of the L*a*b*gamut, Bruce Lindbloom.

      [21] G. Loy, Fast shape-based road sign detection for a driver assistance system. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, pp. 70–75.

      [22] P. Viola and M. Jones, Robust real-time object detection. In International Journal of Computer Vision, 2001.

      [23] K. Brki´c, A. Pinz, and S. ˇSegvi´c, Traffic sign detection as a component of an automated traffic infrastructure inventory system. 2009.

      [24] N Mohd Ali, MS Karis, SA Ahmad Tarusan, Gao-Jie Wong, MS Mohd Aras, MB Bahar, AF Zainal Abidin, Inspection and Quality Checking of Ceramic Cup using Machine Vision Technique: Design and Analysis. Jurnal Teknologi, pp. 33-38, 2017.

      [25] N Mohd Ali, MS Karis, NM Mohd Sobran, MB Bahar, Oh Kok Ken, M Mat Ibrahim, NF Johan, Detection of Multiple Mangoes using Histogram of Oriented Gradient Technique in Aerial Monitoring. ARPN Journal of Engineering and Applied Sciences, pp. 2730-2736, 2017.

      [26] MS Karis, N Mohd Ali, A Mohd Basar, HI Jaafar, AF Zainal Abidin, An Analysis on out-of-plane Face Detection among Female Student and Illumination effects using SIFT and SUFT. AIP Conference Proceedings, 2016.

      [27] MS Karis, N Mohd Ali, WH Mohd Saad, AF Zainal Abidin, N Ismaun, M Abd Aziz, Performance Analysis between Keypoints of SURF and Skin Colour YCBCR based Technique for Face Detection among Final Year UTeM Male Student. pp. 65-69, Jurnal Teknologi, 2017.

      [28] N Mohd Ali, MS Karis, AF Zainal Abidin, B Bakri, NR Abd Razif, Traffic Sign Detection and Recognition: Review and Analysis. pp. 107-113, Jurnal Teknologi, 2017.




Article ID: 16899
DOI: 10.14419/ijet.v7i3.14.16899

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